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Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

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4 Modern databases Every Developer Should Know

Below is the complete list of NoSQL databases currently available in the market.

NoSQL Databases
NoSQL Databases

1. Sorted Order Column Oriented Stores

  1. Google's Bigtable espouses a model where data is stored in a column-oriented way. This contrasts with the row-oriented format in RDBMS.
  2. The column-oriented storage allows data to be stored effectively. It avoids consuming space when storing nulls by simply not storing a column when a value doesn't exist for that column.
  3. Each unit of data can be thought of as a set of key/value pairs, where the unit itself is identified with the help of a primary identifier, often referred to as the primary key. Bigtable and its clones tend to call this primary key to the row-key.
Example:
The name column-family bucket stores the following values:
 For row-key: 1
 first_name: John
 last_name: Doe
 For row-key: 2
 first_name: Jane
The location column-family stores the following:
For row-key: 1
zip_code: 10001
For row-key: 2
zip_code: 94303
The profile column-family has values only for the data point with row-key 1 so it stores only the following:
 For row-key: 1
 gender: male
Databases under this category:
  • Hyper Table
  • Cloudera
  • Hbase

2. Key/Value Pair Stores

  1. HashMap or an associative array is the simplest data structure that can hold a set of key/value pairs. Such data structures are extremely popular because they provide a very efficient, big O(1) average algorithm running time for accessing data.
  2. The key of a key/value pair is a unique value in the set and can be easily looked up to access the data.
  3. Key/value pairs are of varied types: some keep the data in memory and some provide the capability to persist the data to disk. Key/value pairs can be distributed and held in a cluster of nodes.
  4. Oracle's Berkeley DB. Berkeley DB is a pure storage engine where both key and value are an array of bytes. The core storage engine of Berkeley DB doesn't attach meaning to the key or the value. It takes byte array pairs in and returns the same back to the calling client.
  5. Berkeley DB allows data to be cached in memory and flushed to disk as it grows. There is also a notion of indexing the keys for faster lookup and access.
Databases under this category:
  • Membase
  • Kyoto Cabinet
  • Redis
  • Cassandra
  • Voldemart
  • Riak

3. Document Databases

  1. Document databases are not document management systems. More often than not, developers starting out with NoSQL confuse document databases with document and content management systems. The word document in document databases connotes loosely structured sets of key/ value pairs in documents, typically JSON (JavaScript Object Notation), and not documents or spreadsheets (though these could be stored too).
  2. Document databases treat a document as a whole and avoid splitting a document into its constituent name/value pairs. At a collection level, this allows for putting together a diverse set of documents into a single collection. Document databases allow indexing of documents on the basis of not only its primary identifier but also its properties. 
  3. A few different open-source document databases are available today but the most prominent among the available options are MongoDB and CouchDB.
Databases under this category:
  • MongoDB
  • CouchDB

4. Graph Database

Graph databases and XML data stores could also qualify as NoSQL databases.
Databases under this category:
  • Neo4j
  • FlockDB

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